4 citations · 4 across the 2 of their papers we have counts for
5 papers
Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness
Caner Hazirbas, Yejin Bang, Tiezheng Yu +9
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, t…
Localized Uncertainty Attacks
Ousmane Amadou Dia, Theofanis Karaletsos, Caner Hazirbas +3
The susceptibility of deep learning models to adversarial perturbations has stirred renewed attention in adversarial examples resulting in a number of attacks. However, most of the…
Towards Measuring Fairness in AI: the Casual Conversations Dataset
Caner Hazirbas, Joanna Bitton, Brian Dolhansky +3
This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and…
Adversarial Threats to DeepFake Detection: A Practical Perspective
Paarth Neekhara, Brian Dolhansky, Joanna Bitton +1
Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the…
Preserving Integrity in Online Social Networks
Alon Halevy, Cristian Canton Ferrer, Hao Ma +5
Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinforma…